2026-09-21T09:03:30Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/14760 2023-07-10 col_2142_8888 col_2142_5131 com_2142_8887 com_2142_234 com_2142_5130

Huang, Thomas S. Huang, Thomas S. Dikmen, Mert 2010-01-06T17:50:07Z 2010-01-06T17:50:07Z 2012-01-07T11:00:14Z 2010-01-06T17:50:07Z Automated surveillance has long been an application goal of computer vision. An integral part of such surveillance systems is concerned with accurately segmenting foreground objects from the static background in the videos. In this thesis we introduce a novel system for background subtraction, which takes a di erent approach than the conventional background subtraction systems. We make the assumption that the video background is stationary and the foreground objects take up only a small portion of the entire frame at any given time. This speci c assumption allows us to formulate the foreground signal as a sparse additive error introduced to otherwise clean background signal. We outline the algorithm for performing background subtraction using linear programming, and demonstrate accurate segmentations of foreground objects under realistic surveillance scenarios. The proposed method is on par with the state of the art approaches for accurately segmenting the foreground under challenging conditions. Furthermore we propose several methods for building a set of bases to represent the background and provide empirical justi cation of their e ectiveness. Item withdrawn by Mark Zulauf (zulauf@illinois.edu) on 2009-11-28T21:05:25Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 1 Dikmen_Mert.pdf: 1229227 bytes, checksum: 38ef5e61227dbbf39ca62ee4690235e1 (MD5) 2009-12 Made available in DSpace on 2010-01-06T17:50:07Z (GMT). No. of bitstreams: 2 license.txt: 4060 bytes, checksum: 47c8d00affc91984b328d8ca7455e770 (MD5) Dikmen_Mert.pdf: 1229227 bytes, checksum: 38ef5e61227dbbf39ca62ee4690235e1 (MD5) Item marked as restricted to the 'Administrator' Group (id=1) by William Ingram (wingram2@illinois.edu) on 2010-01-06T17:50:32Z Item is restricted until 2012-01-06T17:50:31Z Item reinstated by Sarah Shreeves (sshreeve@illinois.edu) on 2012-01-07T11:00:14Z Item was in collections: Dissertations and Theses - Electrical and Computer Engineering (ID: 446) University of Illinois Dissertations and Theses (ID: 204) No. of bitstreams: 3 Dikmen_Mert.pdf.txt: 57965 bytes, checksum: 83d4e38d54bd0f5e227719c284bf48dd (MD5) license.txt: 4060 bytes, checksum: 47c8d00affc91984b328d8ca7455e770 (MD5) Dikmen_Mert.pdf: 1229227 bytes, checksum: 38ef5e61227dbbf39ca62ee4690235e1 (MD5) Item released from any restrictions by Sarah Shreeves (sshreeve@illinois.edu) on 2012-01-07T11:00:14Z http://hdl.handle.net/2142/14760 en Copyright 2009 Mert Dikmen computer vision background subtraction sparse representation linear decoding automatic surveillance A foreground detection system for automatic surveillance Electrical & Computer Eng 1933 Electrical & Computer Engr 1200 University of Illinois at Urbana-Champaign Thesis M.S. MS:Electr & Computer Eng-UIUC 10KS1200MS